Описание проекта

Оператор связи «Ниединогоразрыва.ком» хочет научиться прогнозировать отток клиентов. Если выяснится, что пользователь планирует уйти, ему будут предложены промокоды и специальные условия. Команда оператора собрала персональные данные о некоторых клиентах, информацию об их тарифах и договорах.

Описание услуг

Оператор предоставляет два основных типа услуг:

1. Стационарную телефонную связь. Возможно подключение телефонного аппарата к нескольким линиям одновременно.
2. Интернет. Подключение может быть двух типов: через телефонную линию (DSL, от англ. digital subscriber line, «цифровая абонентская линия») или оптоволоконный кабель (Fiber optic).

Также доступны такие услуги:

• Интернет-безопасность: антивирус (DeviceProtection) и блокировка небезопасных сайтов (OnlineSecurity);
• Выделенная линия технической поддержки (TechSupport);
• Облачное хранилище файлов для резервного копирования данных (OnlineBackup);
• Стриминговое телевидение (StreamingTV) и каталог фильмов (StreamingMovies).

За услуги клиенты могут платить каждый месяц или заключить договор на 1–2 года. Доступны различные способы расчёта и возможность получения электронного чека.

Описание данных

Данные состоят из файлов, полученных из разных источников:

• contract_new.csv — информация о договоре;
• personal_new.csv — персональные данные клиента;
• internet_new.csv — информация об интернет-услугах;
• phone_new.csv — информация об услугах телефонии.

Во всех файлах столбец customerID содержит код клиента.

Информация о договорах актуальна на 1 февраля 2020.

План работы

1. Знакомство с данными. Необходимо выгрузить и проверить типы данных и наличие пропусков.
2. Предобработка данных. На этом этапе нужно подготовить данные. В данном случае требуется обьединить датафреймы по ключу customerID, избавиться от пропусков в последствии, изменить типы данных и привести названия столбцов к нижнему регистру.
3. Исследовательский анализ данных. Необходимо визуализировать данные, исследовать корреляцию между признаками и избавиться от выбросов.
4. Подготовить данные для обучения модели. Удалить,если есть, ненужные столбцы, разделить данные на выборки, закодировать категориальные признаки с помощью OneHotEncoder, для линейных моделей масштабировать числовые признаки.
5. Подбор гиперпараметров и обучение 2-3 моделей.
6. Тестирование наилучшей модели.
7. Вывод.

Цель работы

Прогноз оттока клиентов для предложения им ряда дополнительных услуг или бонусов для оптимизации и регулировки количества пользователей данного оператора связи, следствием чего является увеличение/сохранение выручки компании.

Знакомство с данными

Импорт библиотек

%pip install phik%pip install xgboost%pip install lightgbm

import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
from scipy import stats as st
from scipy.stats import f_oneway
from scipy.stats import spearmanr

import seaborn as sns

import phik
from phik.report import plot_correlation_matrix
from phik import report

from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn.preprocessing import OneHotEncoder

from catboost import CatBoostClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier

import warnings

warnings.filterwarnings('ignore')

Чтение файлов с датасетами

try:
    contract = pd.read_csv('contract_new.csv')
    personal = pd.read_csv('personal_new.csv')
    internet = pd.read_csv('internet_new.csv')
    phone = pd.read_csv('phone_new.csv')
except:
    contract = pd.read_csv('/datasets/contract_new.csv')
    personal = pd.read_csv('/datasets/personal_new.csv')
    internet = pd.read_csv('/datasets/internet_new.csv')
    phone = pd.read_csv('/datasets/phone_new.csv')

Напишем функцию для оптимизации просмотра данных

def info(df):
    display(df.info())
    display(df.head(10))
    display(df.describe())

Познакомимся с фреймом contract

info(contract)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 8 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   BeginDate         7043 non-null   object 
 2   EndDate           7043 non-null   object 
 3   Type              7043 non-null   object 
 4   PaperlessBilling  7043 non-null   object 
 5   PaymentMethod     7043 non-null   object 
 6   MonthlyCharges    7043 non-null   float64
 7   TotalCharges      7043 non-null   object 
dtypes: float64(1), object(7)
memory usage: 440.3+ KB

None

	customerID	BeginDate	EndDate	Type	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges
0	7590-VHVEG	2020-01-01	No	Month-to-month	Yes	Electronic check	29.85	31.04
1	5575-GNVDE	2017-04-01	No	One year	No	Mailed check	56.95	2071.84
2	3668-QPYBK	2019-10-01	No	Month-to-month	Yes	Mailed check	53.85	226.17
3	7795-CFOCW	2016-05-01	No	One year	No	Bank transfer (automatic)	42.30	1960.6
4	9237-HQITU	2019-09-01	No	Month-to-month	Yes	Electronic check	70.70	353.5
5	9305-CDSKC	2019-03-01	No	Month-to-month	Yes	Electronic check	99.65	1150.96
6	1452-KIOVK	2018-04-01	No	Month-to-month	Yes	Credit card (automatic)	89.10	2058.21
7	6713-OKOMC	2019-04-01	No	Month-to-month	No	Mailed check	29.75	300.48
8	7892-POOKP	2017-07-01	No	Month-to-month	Yes	Electronic check	104.80	3573.68
9	6388-TABGU	2014-12-01	2017-05-01	One year	No	Bank transfer (automatic)	56.15	1628.35

	MonthlyCharges
count	7043.000000
mean	64.761692
std	30.090047
min	18.250000
25%	35.500000
50%	70.350000
75%	89.850000
max	118.750000

Познакомимся с фреймом personal

info(personal)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 5 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   customerID     7043 non-null   object
 1   gender         7043 non-null   object
 2   SeniorCitizen  7043 non-null   int64 
 3   Partner        7043 non-null   object
 4   Dependents     7043 non-null   object
dtypes: int64(1), object(4)
memory usage: 275.2+ KB

None

	customerID	gender	SeniorCitizen	Partner	Dependents
0	7590-VHVEG	Female	0	Yes	No
1	5575-GNVDE	Male	0	No	No
2	3668-QPYBK	Male	0	No	No
3	7795-CFOCW	Male	0	No	No
4	9237-HQITU	Female	0	No	No
5	9305-CDSKC	Female	0	No	No
6	1452-KIOVK	Male	0	No	Yes
7	6713-OKOMC	Female	0	No	No
8	7892-POOKP	Female	0	Yes	No
9	6388-TABGU	Male	0	No	Yes

	SeniorCitizen
count	7043.000000
mean	0.162147
std	0.368612
min	0.000000
25%	0.000000
50%	0.000000
75%	0.000000
max	1.000000

Познакомимся с фреймом internet

info(internet)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5517 entries, 0 to 5516
Data columns (total 8 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   customerID        5517 non-null   object
 1   InternetService   5517 non-null   object
 2   OnlineSecurity    5517 non-null   object
 3   OnlineBackup      5517 non-null   object
 4   DeviceProtection  5517 non-null   object
 5   TechSupport       5517 non-null   object
 6   StreamingTV       5517 non-null   object
 7   StreamingMovies   5517 non-null   object
dtypes: object(8)
memory usage: 344.9+ KB

None

	customerID	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies
0	7590-VHVEG	DSL	No	Yes	No	No	No	No
1	5575-GNVDE	DSL	Yes	No	Yes	No	No	No
2	3668-QPYBK	DSL	Yes	Yes	No	No	No	No
3	7795-CFOCW	DSL	Yes	No	Yes	Yes	No	No
4	9237-HQITU	Fiber optic	No	No	No	No	No	No
5	9305-CDSKC	Fiber optic	No	No	Yes	No	Yes	Yes
6	1452-KIOVK	Fiber optic	No	Yes	No	No	Yes	No
7	6713-OKOMC	DSL	Yes	No	No	No	No	No
8	7892-POOKP	Fiber optic	No	No	Yes	Yes	Yes	Yes
9	6388-TABGU	DSL	Yes	Yes	No	No	No	No

	customerID	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies
count	5517	5517	5517	5517	5517	5517	5517	5517
unique	5517	2	2	2	2	2	2	2
top	7590-VHVEG	Fiber optic	No	No	No	No	No	No
freq	1	3096	3498	3088	3095	3473	2810	2785

Познакомимся с фреймом phone

info(phone)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6361 entries, 0 to 6360
Data columns (total 2 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   customerID     6361 non-null   object
 1   MultipleLines  6361 non-null   object
dtypes: object(2)
memory usage: 99.5+ KB

None

	customerID	MultipleLines
0	5575-GNVDE	No
1	3668-QPYBK	No
2	9237-HQITU	No
3	9305-CDSKC	Yes
4	1452-KIOVK	Yes
5	7892-POOKP	Yes
6	6388-TABGU	No
7	9763-GRSKD	No
8	7469-LKBCI	No
9	8091-TTVAX	Yes

	customerID	MultipleLines
count	6361	6361
unique	6361	2
top	5575-GNVDE	No
freq	1	3390

Вывод

1. Contract
   • В столбце BeginDate необходимо изменить изменить тип данных на datetime64;
   • В столбце TotalCharges необходимо изменить изменить тип данных на float64;
   • EndDate является целевым признаком. Тут необходимо заменить No на дату сбора данных (1 февраля 2020);
   • PaperlessBilling, Type, PaymentMethod являются категориальными признаками и нуждаются в кодировке.
2. Personal
   • Gender, Partner и Dependents являются категориальными признаками и нуждаются в кодировке.
3. Internet
   • Всем столбцам кроме customerID требуется кодировка.
4. Phone
   • MultipleLines требуется кодировка.
5. Общий вывод по данным
   • В данных нет пропусков, но во фреймах Internet и Phone есть данные не обо всех клиентах, что может привести к потере данных при обьединении фреймов.

Предобработка данных

Для начала добавим признак ухода клиента

contract['tag'] = (contract['EndDate'] != 'No').astype(int)
contract.head(10)

	customerID	BeginDate	EndDate	Type	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	tag
0	7590-VHVEG	2020-01-01	No	Month-to-month	Yes	Electronic check	29.85	31.04	0
1	5575-GNVDE	2017-04-01	No	One year	No	Mailed check	56.95	2071.84	0
2	3668-QPYBK	2019-10-01	No	Month-to-month	Yes	Mailed check	53.85	226.17	0
3	7795-CFOCW	2016-05-01	No	One year	No	Bank transfer (automatic)	42.30	1960.6	0
4	9237-HQITU	2019-09-01	No	Month-to-month	Yes	Electronic check	70.70	353.5	0
5	9305-CDSKC	2019-03-01	No	Month-to-month	Yes	Electronic check	99.65	1150.96	0
6	1452-KIOVK	2018-04-01	No	Month-to-month	Yes	Credit card (automatic)	89.10	2058.21	0
7	6713-OKOMC	2019-04-01	No	Month-to-month	No	Mailed check	29.75	300.48	0
8	7892-POOKP	2017-07-01	No	Month-to-month	Yes	Electronic check	104.80	3573.68	0
9	6388-TABGU	2014-12-01	2017-05-01	One year	No	Bank transfer (automatic)	56.15	1628.35	1

Заменим в EndDate значения "No" датой выгрузки датасета

contract['EndDate'] = contract['EndDate'].replace(['No'], ['2020-02-01'])

Заменим тип данных в BeginDate и EndDate

contract['BeginDate'] = pd.to_datetime(contract['BeginDate'], format='%Y-%m-%d')
contract['EndDate'] = pd.to_datetime(contract['EndDate'], format='%Y-%m-%d')
contract.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 9 columns):
 #   Column            Non-Null Count  Dtype         
---  ------            --------------  -----         
 0   customerID        7043 non-null   object        
 1   BeginDate         7043 non-null   datetime64[ns]
 2   EndDate           7043 non-null   datetime64[ns]
 3   Type              7043 non-null   object        
 4   PaperlessBilling  7043 non-null   object        
 5   PaymentMethod     7043 non-null   object        
 6   MonthlyCharges    7043 non-null   float64       
 7   TotalCharges      7043 non-null   object        
 8   tag               7043 non-null   int64         
dtypes: datetime64[ns](2), float64(1), int64(1), object(5)
memory usage: 495.3+ KB

Заменим тип данных в TotalCharges

contract['TotalCharges'] = pd.to_numeric(contract['TotalCharges'], errors ='coerce')
contract['TotalCharges'].isnull().sum()

11

Взглянем, что в чем проблема с пропусками

contract[contract['TotalCharges'].isnull()]

	customerID	BeginDate	EndDate	Type	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	tag
488	4472-LVYGI	2020-02-01	2020-02-01	Two year	Yes	Bank transfer (automatic)	52.55	NaN	0
753	3115-CZMZD	2020-02-01	2020-02-01	Two year	No	Mailed check	20.25	NaN	0
936	5709-LVOEQ	2020-02-01	2020-02-01	Two year	No	Mailed check	80.85	NaN	0
1082	4367-NUYAO	2020-02-01	2020-02-01	Two year	No	Mailed check	25.75	NaN	0
1340	1371-DWPAZ	2020-02-01	2020-02-01	Two year	No	Credit card (automatic)	56.05	NaN	0
3331	7644-OMVMY	2020-02-01	2020-02-01	Two year	No	Mailed check	19.85	NaN	0
3826	3213-VVOLG	2020-02-01	2020-02-01	Two year	No	Mailed check	25.35	NaN	0
4380	2520-SGTTA	2020-02-01	2020-02-01	Two year	No	Mailed check	20.00	NaN	0
5218	2923-ARZLG	2020-02-01	2020-02-01	One year	Yes	Mailed check	19.70	NaN	0
6670	4075-WKNIU	2020-02-01	2020-02-01	Two year	No	Mailed check	73.35	NaN	0
6754	2775-SEFEE	2020-02-01	2020-02-01	Two year	Yes	Bank transfer (automatic)	61.90	NaN	0

Вероятно это новые пользователи. Заменим пропуски в этом стлолбце на 0

contract['TotalCharges'] = contract['TotalCharges'].fillna(0)

Обьеденим все датафреймы в один и проиндексируем фрейм по customerID

df = contract \
    .merge(personal, how='left', on='customerID') \
    .merge(internet, how='left', on='customerID') \
    .merge(phone, how='left', on='customerID') 

df = df.set_index('customerID')

df.head()

	BeginDate	EndDate	Type	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	tag	gender	SeniorCitizen	Partner	Dependents	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	MultipleLines
customerID
7590-VHVEG	2020-01-01	2020-02-01	Month-to-month	Yes	Electronic check	29.85	31.04	0	Female	0	Yes	No	DSL	No	Yes	No	No	No	No	NaN
5575-GNVDE	2017-04-01	2020-02-01	One year	No	Mailed check	56.95	2071.84	0	Male	0	No	No	DSL	Yes	No	Yes	No	No	No	No
3668-QPYBK	2019-10-01	2020-02-01	Month-to-month	Yes	Mailed check	53.85	226.17	0	Male	0	No	No	DSL	Yes	Yes	No	No	No	No	No
7795-CFOCW	2016-05-01	2020-02-01	One year	No	Bank transfer (automatic)	42.30	1960.60	0	Male	0	No	No	DSL	Yes	No	Yes	Yes	No	No	NaN
9237-HQITU	2019-09-01	2020-02-01	Month-to-month	Yes	Electronic check	70.70	353.50	0	Female	0	No	No	Fiber optic	No	No	No	No	No	No	No

Пропуски в пунктах 12-19 вероятнее всего означают отстутствие этого сервиса у клиента. Заменим этим пропуски "No"

columns = ['InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'MultipleLines']
for i in columns:
    df[i] = df[i].fillna('No')

df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 7043 entries, 7590-VHVEG to 3186-AJIEK
Data columns (total 20 columns):
 #   Column            Non-Null Count  Dtype         
---  ------            --------------  -----         
 0   BeginDate         7043 non-null   datetime64[ns]
 1   EndDate           7043 non-null   datetime64[ns]
 2   Type              7043 non-null   object        
 3   PaperlessBilling  7043 non-null   object        
 4   PaymentMethod     7043 non-null   object        
 5   MonthlyCharges    7043 non-null   float64       
 6   TotalCharges      7043 non-null   float64       
 7   tag               7043 non-null   int64         
 8   gender            7043 non-null   object        
 9   SeniorCitizen     7043 non-null   int64         
 10  Partner           7043 non-null   object        
 11  Dependents        7043 non-null   object        
 12  InternetService   7043 non-null   object        
 13  OnlineSecurity    7043 non-null   object        
 14  OnlineBackup      7043 non-null   object        
 15  DeviceProtection  7043 non-null   object        
 16  TechSupport       7043 non-null   object        
 17  StreamingTV       7043 non-null   object        
 18  StreamingMovies   7043 non-null   object        
 19  MultipleLines     7043 non-null   object        
dtypes: datetime64[ns](2), float64(2), int64(2), object(14)
memory usage: 1.1+ MB

Вывод

• Добавлен целевой признак ухода/неухода клиента
• Заменили в EndDate значения "No" датой выгрузки датасета
• Заменили тип данных в BeginDate, EndDate и TotalCharges и избавились от попусков
• Обьеденили датасеты и устранили пропуски

Теперь в данных порядок. Приводить названия столбцов к нижненму регистру не стала, так как в целом в этом нет необходимости

Исследовательский анализ данных

Построим гистограммы и диаграммы размаха для числовых данных

col = 2
row = 3
columns = ['MonthlyCharges', 'TotalCharges']

plt.figure(figsize=(20, 20))

j = 0

for i in columns:
    j += 1 
    plt.subplot(row, col, j)
    plt.hist([df[i].loc[df['tag'] == 1], df[i].loc[df['tag'] == 0]], label=['Ушедшие', 'Оставшиеся'], color=['lightblue', 'pink'])
    plt.title(i)
    plt.legend()
    
for i in columns:
    j += 1 

    plt.subplot(row, col, j)
    plt.boxplot(df[i])
    plt.title(i)

plt.show()

Значения в MonthlyCharges распредедяются неравномерно. Есть пики на 20 и 80. Кажется такое распределение значений может говорить о наличии тарифов у оператора или каких то других "групп" клиентов. Гистограмма TotalCharges равномерно убывает. Аномальных выбросов я не увидела.

df[columns].describe()

	MonthlyCharges	TotalCharges
count	7043.000000	7043.000000
mean	64.761692	2115.312885
std	30.090047	2112.742814
min	18.250000	0.000000
25%	35.500000	436.750000
50%	70.350000	1343.350000
75%	89.850000	3236.690000
max	118.750000	9221.380000

Попробуем провести статистический тест. Для определения зависимости категориальный признак - числовой признак воспользуемся ANOVA.

Нулевая гипотеза(H0): Признаки не коррелируют между собой.
Гипотиза подтверждается только тогда, когда P-Value > 0,05

for i in columns:
    print(i)
    CategoryGroupLists=df.groupby(i)['tag'].apply(list)
    AnovaResults = f_oneway(*CategoryGroupLists)
    print('P-Value for Anova is: ', AnovaResults[1])

MonthlyCharges
P-Value for Anova is:  3.2066248562743217e-07
TotalCharges
P-Value for Anova is:  7.611976777680091e-11

Гипотеза отвергается. Признаки коррелируют с нашим целевым признаком.

Взглянем на корреляцию этих признаков между собой

df[columns].corr()

	MonthlyCharges	TotalCharges
MonthlyCharges	1.00000	0.63044
TotalCharges	0.63044	1.00000

rho, p = spearmanr(df['MonthlyCharges'], df['TotalCharges'])
print('rho=',rho, 'p=', p)

rho= 0.6464276837229888 p= 0.0

Признаки коррелируют, что логично. Чем больше месячный платеж, тем больше общая сумма покупок. Один из них нужно убрать, но пока не понятно какой именно.

Далее посмотрим на категориальные данные

cat_columns = ['Type','PaperlessBilling','PaymentMethod','gender','SeniorCitizen','Partner','Dependents',
                      'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
                      'StreamingTV', 'StreamingMovies', 'MultipleLines']
for j in cat_columns:
    sns.catplot(data=df, x='tag', hue=j, kind='count', palette=sns.color_palette("vlag"))

• Оплата по месяцам в два раза популярнее, чем оплата сразу за один/два года, а вот у ушедших клиентов показатели примерно на одном уровне.
• У ушедших клиентов в MultipleLines соотношение подключивших и неподключивших эту услугу противоположно оставшимся клиентам. Та же ситуация у StreamingMovies, StreamingTV, Partner.
• Так же отличаются распределения в колонках OnlineBackup, PaymentMethod (по одному пункту) и DeviceProtection.
• Столбец gender никак не скажется на результатах, так как выборка по мужчинам и женщинам равномерная. Позже удалим его.

Посмотрим корреляцию категориальных признаков

phik_overview = df[cat_columns].phik_matrix()
plot_correlation_matrix(phik_overview.values, 
                        x_labels=phik_overview.columns, 
                        y_labels=phik_overview.index, 
                        vmin=0, vmax=1, color_map='PuBu', 
                        title=r"correlation $\phi_K$", 
                        fontsize_factor=1.5, 
                        figsize=(20, 20))
plt.tight_layout()

interval columns not set, guessing: ['SeniorCitizen']

Далее взглянем на корреляюцию всех от всех

phik_overview = df.phik_matrix()
plot_correlation_matrix(phik_overview.values, 
                        x_labels=phik_overview.columns, 
                        y_labels=phik_overview.index, 
                        vmin=0, vmax=1, color_map='PuBu', 
                        title=r"correlation $\phi_K$", 
                        fontsize_factor=1.5, 
                        figsize=(20, 20))
plt.tight_layout()

interval columns not set, guessing: ['MonthlyCharges', 'TotalCharges', 'tag', 'SeniorCitizen']

Корреляция между StreamingTV и StreamingMovies 0.74.
Тоже касается Partner и Dependents. Удалять мы их не будем пока. Так же наблюдается сильная корреляция в столбцах с датами. Для обучения они нам не нужны, а испортить модель могут. Удалим оба столбца, предварительно создав столбец с общим количеством дней пользования.

df['Days'] = (df['EndDate'] - df['BeginDate']).dt.days

del df['BeginDate'], df['EndDate'], df['gender']

Теперь вернемся к столбцам MonthlyCharges и TotalCharges. Посмотрим на корреляцию со столбцом Days.

columns = ['MonthlyCharges', 'TotalCharges', 'Days']
df[columns].corr()

	MonthlyCharges	TotalCharges	Days
MonthlyCharges	1.000000	0.630440	0.220054
TotalCharges	0.630440	1.000000	0.821746
Days	0.220054	0.821746	1.000000

TotalCharges проиграл эту битву. Возьмем на заметку, но удалять пока не будем.

Вывод

• Значения в MonthlyCharges распредедяются неравномерно. Есть пики на 20 и 80. Кажется такое распределение значений может говорить о наличии тарифов у оператора или каких то других "групп" клиентов. Гистограмма TotalCharges равномерно убывает. Аномальных выбросов я не увидела.
• Обнаружили корреляцию у MonthlyCharges и TotalCharges.
• Оплата по месяцам в два раза популярнее, чем оплата сразу за один/два года, а вот у ушедших клиентов показатели примерно на одном уровне.
• У ушедших клиентов в MultipleLines соотношение подключивших и неподключивших эту услугу противоположно оставшимся клиентам. Та же ситуация у StreamingMovies, StreamingTV, Partner.
• Так же отличаются распределения в колонках OnlineBackup, PaymentMethod (по одному пункту) и DeviceProtection.
• Столбец gender никак не скажется на результатах, так как выборка по мужчинам и женщинам равномерная.
• Обнаружена корреляция между StreamingTV и StreamingMovies в 0.74, а также Partner и Dependents.
• Наблюдается также корреляция целевого признака и столбцов с датами. Удалили их, предварительно создав новый столбец со временем пользования даннным оперетором.

Подготовка данных для обучения

Разделим датафрейм на обучающую и тестовую выбороки

RANDOM_STATE = 50623
target = df['tag'] 
features = df.drop(columns=['tag'])
features_train, features_test, target_train, target_test = train_test_split(
    features, target, test_size=0.25, random_state=RANDOM_STATE)

Закодируем категориальные данные с помощью One-Hot Encoding

features_train_not_ohe = features_train
features_test_not_ohe = features_test

cat_columns = features_train.select_dtypes(include='object').columns.to_list()
numeric = features_train.select_dtypes(exclude='object').columns.to_list()

cat_columns

['Type',
 'PaperlessBilling',
 'PaymentMethod',
 'Partner',
 'Dependents',
 'InternetService',
 'OnlineSecurity',
 'OnlineBackup',
 'DeviceProtection',
 'TechSupport',
 'StreamingTV',
 'StreamingMovies',
 'MultipleLines']

numeric

['MonthlyCharges', 'TotalCharges', 'SeniorCitizen', 'Days']

from sklearn.preprocessing import OneHotEncoder

encoder_ohe = OneHotEncoder(drop='first', handle_unknown='ignore', sparse=False)
encoder_ohe.fit(features_train[cat_columns])
new_columns = encoder_ohe.get_feature_names(cat_columns)
features_train[new_columns] = encoder_ohe.transform(features_train[cat_columns])
features_train = features_train.drop(cat_columns, axis=1)
features_test[new_columns] = encoder_ohe.transform(features_test[cat_columns])
features_test = features_test.drop(cat_columns, axis=1)
features_train.head()
features_test.head()

	MonthlyCharges	TotalCharges	SeniorCitizen	Days	Type_One year	Type_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	...	Dependents_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_Yes	OnlineBackup_Yes	DeviceProtection_Yes	TechSupport_Yes	StreamingTV_Yes	StreamingMovies_Yes	MultipleLines_Yes
customerID
1608-GMEWB	93.90	4225.50	1	1371	0.0	0.0	1.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	1.0	1.0
2070-FNEXE	76.45	636.06	1	245	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
3771-PZOBW	90.70	1814.00	0	610	0.0	0.0	1.0	1.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0
2930-UOTMB	65.25	1175.81	0	518	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	1.0
2951-QOQTK	80.80	404.00	0	153	0.0	0.0	0.0	1.0	0.0	0.0	...	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0

5 rows × 21 columns

features_train.head()

	MonthlyCharges	TotalCharges	SeniorCitizen	Days	Type_One year	Type_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	...	Dependents_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_Yes	OnlineBackup_Yes	DeviceProtection_Yes	TechSupport_Yes	StreamingTV_Yes	StreamingMovies_Yes	MultipleLines_Yes
customerID
5949-EBSQK	61.45	307.25	0	153	0.0	0.0	0.0	1.0	0.0	0.0	...	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0
4760-XOHVN	19.45	38.90	0	62	0.0	0.0	0.0	0.0	0.0	1.0	...	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2607-FBDFF	49.00	49.00	0	31	0.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
3125-RAHBV	20.30	455.53	0	671	1.0	0.0	0.0	1.0	0.0	0.0	...	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2829-HYVZP	19.80	574.20	0	883	1.0	0.0	1.0	0.0	0.0	1.0	...	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

5 rows × 21 columns

features_train.columns == features_test.columns

array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True])

Масштабируем количественные признаки

columns = ['MonthlyCharges', 'Days', 'TotalCharges']

scaler = StandardScaler()

scaler.fit(features_train[columns])

features_train = features_train.copy()
features_train[columns] = scaler.transform(features_train[columns])

features_test = features_test.copy()
features_test[columns] = scaler.transform(features_test[columns])

Вывод

• Разделили выборку на обучающую и тестовую с параметрами test_size=0.25 и random_state=50623
• Закондировали категориальные признаки с помощью One-Hot Encoding
• Масштабировали количественные признаки с помощью StandardScaler

Обучение моделей

Модель LogisticRegression

%%time
model = LogisticRegression(random_state = RANDOM_STATE, solver='lbfgs', class_weight='balanced')
param = { 'C': list(range(1,15,3))}
grid = GridSearchCV(model, param_grid=param, scoring='roc_auc', cv=2, verbose=True, n_jobs=-1)
model = grid.fit(features_train, target_train)
model_LR = model.best_estimator_
score_LR = model.best_score_
print('Best parameters:', model.best_params_)
print('Best score:', score_LR)

Fitting 2 folds for each of 5 candidates, totalling 10 fits
Best parameters: {'C': 1}
Best score: 0.7686979603288087
CPU times: user 118 ms, sys: 23 ms, total: 141 ms
Wall time: 218 ms

Модель RandomForestClassifier

%%time

model = RandomForestClassifier(random_state = RANDOM_STATE, class_weight='balanced')
param = {'n_estimators': list(range(50,300,50)), 'max_depth':[5,15], 'max_features':['sqrt', 'log2']}
grid = GridSearchCV(model, param_grid=param, scoring='roc_auc', cv=3, verbose=True, n_jobs=-1)
model = grid.fit(features_train, target_train)
model_RFC = model.best_estimator_
score_RFC = model.best_score_
print('Best parameters:', model.best_params_)
print('Best score:', score_RFC)

Fitting 3 folds for each of 20 candidates, totalling 60 fits
Best parameters: {'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 100}
Best score: 0.8186194183639643
CPU times: user 664 ms, sys: 122 ms, total: 785 ms
Wall time: 3.8 s

Модель LinearSVC

%%time

model = LinearSVC(random_state=RANDOM_STATE, max_iter=10000, class_weight='balanced', dual = True)
param = {'C': list(range(1,15,3)), 'penalty' : ['l1', 'l2']}
grid = GridSearchCV(model, param_grid=param, scoring='roc_auc', cv=3, verbose=True, n_jobs=-1)
model = grid.fit(features_train, target_train)
model_LSVC = model.best_estimator_
score_LSVC = model.best_score_
print('Best parameters:', model.best_params_)
print('Best score:', score_LSVC)

Fitting 3 folds for each of 10 candidates, totalling 30 fits

/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(
/opt/anaconda3/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  warnings.warn(

Best parameters: {'C': 13, 'penalty': 'l2'}
Best score: 0.7713298991528706
CPU times: user 1.94 s, sys: 33.2 ms, total: 1.98 s
Wall time: 4.93 s

Модель LGBMClassifier

%%time
model = LGBMClassifier(n_jobs=-1, random_state=RANDOM_STATE, class_weight='balanced')
param = {'n_estimators': [300, 500, 1000],
                  'learning_rate': [0.01, 0.1, 0.2, 0.5],
                  'max_depth': range(1, 9)}
grid = GridSearchCV(model, param_grid=param, scoring='roc_auc', cv=3, verbose=0,  n_jobs=-1)
model = grid.fit(features_train, target_train)
model_LGBM = model.best_estimator_
score_LGBM = model.best_score_
print('Best parameters:', model.best_params_)
print('Best score:', score_LGBM)

Best parameters: {'learning_rate': 0.2, 'max_depth': 2, 'n_estimators': 1000}
Best score: 0.8989029883954812
CPU times: user 2.49 s, sys: 234 ms, total: 2.72 s
Wall time: 11.6 s

Модель XGBClassifier

%%time

ratio = target_train[target_train == 1].count() / target_train[target_train == 0].count()

model = XGBClassifier(random_state=RANDOM_STATE, scale_pos_weight=ratio)
param = {'n_estimators': [300, 500, 1000],
                  'learning_rate': [0.01, 0.1, 0.2, 0.5],
                  'max_depth': range(1, 9)}
grid = GridSearchCV(model, param_grid=param, scoring='roc_auc', cv=3, verbose=0,  n_jobs=-1)
model = grid.fit(features_train, target_train)
model_XGB = model.best_estimator_
score_XGB = model.best_score_
print('Best parameters:', model.best_params_)
print('Best score:', score_XGB)

Best parameters: {'learning_rate': 0.5, 'max_depth': 2, 'n_estimators': 1000}
Best score: 0.9134983426707115
CPU times: user 6.31 s, sys: 445 ms, total: 6.75 s
Wall time: 1min 21s

Модель CatBoostClassifier

%%time

cat_features = ['Type', 'PaperlessBilling', 'PaymentMethod',
                'SeniorCitizen', 'Partner', 'InternetService', 'Dependents',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingMovies', 'MultipleLines', 'StreamingTV']

model = CatBoostClassifier(random_state=RANDOM_STATE, auto_class_weights='Balanced', cat_features=cat_features)
param = {'max_depth':range(1, 9), 'learning_rate':[0.01, 0.1, 0.2, 0.5], 'iterations': [300, 500, 1000]}
grid = GridSearchCV(model, param_grid=param, scoring='roc_auc', cv=3, verbose=0,  n_jobs=-1)
model = grid.fit(features_train_not_ohe, target_train)
model_CBC = model.best_estimator_
score_CBC = model.best_score_
print('Best parameters:', model.best_params_)
print('Best score:', score_CBC)

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Best parameters: {'iterations': 1000, 'learning_rate': 0.5, 'max_depth': 2}
Best score: 0.9234442494836443
CPU times: user 6.34 s, sys: 2.64 s, total: 8.98 s
Wall time: 2min 42s

Сделаем сводную таблицу с результатами

index = ['LogisticRegression',
         'RandomForestClassifier',
         'LinearSVC', 'LGBMClassifier', 'XGBClassifier', 'CatBoostClassifier']
data = {'ROC-AUC':[score_LR, score_RFC, score_LSVC, score_LGBM, score_XGB, score_CBC]}

scores_data = pd.DataFrame(data=data, index=index)
scores_data

	ROC-AUC
LogisticRegression	0.768698
RandomForestClassifier	0.818619
LinearSVC	0.771330
LGBMClassifier	0.898903
XGBClassifier	0.913498
CatBoostClassifier	0.923444

Вывод

Лидерами обучения являются модели со следующими показателями:

• LGBMClassifier, ROC-AUC = 0.898903
• XGBClassifier, ROC-AUC = 0.913498
• CatBoostClassifier, ROC-AUC = 0.920756

Для тестирования возмем лучшую модель из представленных с гиперпараметрами:

• CatBoostClassifier, ROC-AUC = 0.920756, iterations=1000, learning_rate=0.5, max_depth=2.

Тестирование лучшей модели

pred_proba_test = model_CBC.predict_proba(features_test_not_ohe)[:, 1]
pred_test = model_CBC.predict(features_test_not_ohe)

print('Accuracy: ', model_CBC.score(features_test_not_ohe, target_test))
print('AUC-ROC: ', roc_auc_score(target_test, pred_proba_test))
fpr, tpr, thresholds = roc_curve(target_test, pred_proba_test)

plt.figure(figsize=(11, 5))
plt.plot(fpr, tpr)
plt.plot([0, 1], [0, 1], linestyle='--')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')

plt.title('ROC-кривая')

plt.show()

Accuracy:  0.9063032367972743
AUC-ROC:  0.9107108081791626

Посмотрим на распределение важности признаков

im_df = pd.DataFrame(model_CBC.feature_importances_, index = features_test_not_ohe.columns, columns=['import'])
im_df = im_df.sort_values(by='import', ascending=False)
im_df

	import
Days	63.284734
TotalCharges	12.378425
MonthlyCharges	11.063366
Type	2.121599
Partner	1.263755
Type_Two year	1.174548
PaymentMethod	1.145428
PaymentMethod_Mailed check	0.934949
MultipleLines_Yes	0.921341
OnlineBackup_Yes	0.697405
Partner_Yes	0.600234
StreamingMovies	0.570096
InternetService	0.499434
Dependents_Yes	0.398773
SeniorCitizen	0.301107
PaymentMethod_Credit card (automatic)	0.288260
MultipleLines	0.213115
DeviceProtection_Yes	0.196044
InternetService_Fiber optic	0.178849
Dependents	0.177195
PaperlessBilling	0.160508
PaperlessBilling_Yes	0.158167
TechSupport_Yes	0.156541
Type_One year	0.145015
DeviceProtection	0.142851
OnlineSecurity_Yes	0.139681
StreamingMovies_Yes	0.124381
PaymentMethod_Electronic check	0.123721
OnlineSecurity	0.114405
OnlineBackup	0.108780
StreamingTV_Yes	0.096732
TechSupport	0.067088
InternetService_No	0.040996
StreamingTV	0.012478

im_df.plot(kind='bar', figsize=(10, 5), title='Важность признаков', color='pink', legend=False)
plt.show()

Вывод

Показатели на тестовой выборке:

• Accuracy: 0.9114139693356048
• AUC-ROC: 0.9083790293650387

Самыми важными признаками являются:

• Время пользования услугами оператора (61.7%),
• Сумма потрачененных средств на услуги (16.7%),
• Ежемесячные траты на услуги (9.6%)

Итоговый вывод

1. В проекте проведена работа по предсказанию отказа клиента от услуг оператора сотовой связи для предоставления первому дополнительных бонусов и сохранения клиента.
2. Проведено знакомство с данными. Выгружены и проверены типы данных и наличие пропусков.
3. Далее проведена предобработка данных. Обьединили датафреймы по ключу customerID, избавились от пропусков в последствии и изменили типы данных.
4. Исследовательский анализ данных. Визуализировали категориальные и количественные признаки. Выявили корреляцию в различных признаках. Убрали столбцы с датами и сгененрировали новый признак с количеством дней использования услуг. На основе предварительного анализа можно сказать, что со временем вероятность отказа от услуг снижается, также чаще от услуг отказываются те, кто больше за них платит.
5. Подготовили данные для обучения модели. Разделили данные на выборки, закодировать категориальные признаки с помощью OneHotEncoder, для линейных моделей масштабировали числовые признаки.
6. Обучение моделей. Был произведен подбор гиперпараметров и обучение следующих моделей: | Model | ROC_AUC | | ------ | ------ | | LogisticRegression | 0.768698 | | RandomForestClassifier | 0.818619 | | LinearSVC | 0.771330 | | LGBMClassifier | 0.898903 | | XGBClassifier | 0.913498 | | CatBoostClassifier | 0.920756 |
7. Тестирование наилучшей модели. Для теста была выбрана модель:

• CatBoostClassifier, ROC-AUC = 0.920756, iterations=1000, learning_rate=0.5, max_depth=2.

Показатели на тестовой выборке:

• Accuracy: 0.9114139693356048
• AUC-ROC: 0.9083790293650387

Самыми важными признаками являются:

• Время пользования услугами оператора (61.7%),
• Сумма потрачененных средств на услуги (16.7%),
• Ежемесячные траты на услуги (9.6%)

1. Рекомендации для оператор связи «Ниединогоразрыва.ком»:

• Стоит обращать особое внимание на новых пользователей. Возможно стоит предлагать новичкам более выгодные тарифы и услуги.
• Также стоит уделить внимание группам пользователей с высокими показателями оплат услуг в месяц. Хорошим вариантом будет предоставлять скидку на оплату за несколько месяцев и год. Хорошим спосбом удержать клиентов - ввести бонусную программу по типу "чем больше платишь - тем больше скидка/бонусы".

Отчет

В проекте были выполнены следующие пункты:

• Знакомство с данными. Выгружены и проверены типы данных и наличие пропусков.
• Предобработка данных. Добавлен целевой признак ухода/неухода клиента. Заменили в EndDate значения "No" датой выгрузки датасета. Заменили тип данных в BeginDate, EndDate и TotalCharges и избавились от попусков. Обьеденили датасеты и устранили пропуски.
• Исследовательский анализ данных. Визуализировали категориальные и количественные признаки. Выявили корреляцию в MonthlyCharges и TotalCharges, StreamingTV и StreamingMovies, Partner и Dependents. Убрали столбцы с датами и сгененрировали новый признак с количеством дней использования услуг. Избавились от столбца gender, так как он не влияет на уход клиента, но может помешать обученнию модели. На основе предварительного анализа предположили, что со временем вероятность отказа от услуг снижается, также чаще от услуг отказываются те, кто больше за них платит.
• Подготовили данные для обучения модели. Разделили данные на выборки с параметрами test_size=0.25, random_state=50623; закодировать категориальные признаки с помощью OneHotEncoder, для линейных моделей масштабировали числовые признаки.
• Обучение моделей. Для обучения были выбраны следующие признаки:

1. Категориальные
   'Type' - тип оплаты, 'PaperlessBilling' - безналичный расчет, 'PaymentMethod' – способ оплаты,, 'Partner' - наличие супруга(и), 'Dependents' - наличие иждивенцев, 'SeniorCitizen' - наличие пенсионного статуса по возрасту, 'InternetService' - интернет сервис, 'OnlineSecurity' - онлайн защита, 'OnlineBackup' - резервное копирование, 'DeviceProtection' - защита устройства, 'TechSupport' - тех поддержка, 'StreamingTV' - ТВ, 'StreamingMovies' - фильмы, 'MultipleLines' - наличие возможности ведения параллельных линий во время звонка.
2. Количественные
   'MonthlyCharges' – ежемесячные траты на услуги, 'TotalCharges' - общие траты, 'Days' - общее количество дней пользования услугами оператора (сгенерированный признак).
3. Целевой
   'tag' - факт ухода клиента (сгенерированный признак).

Был произведен подбор гиперпараметров и обучение следующих моделей:

Model	ROC_AUC
LogisticRegression	0.768698
RandomForestClassifier	0.818619
LinearSVC	0.771330
LGBMClassifier	0.898903
XGBClassifier	0.913498
CatBoostClassifier	0.920756

• Тестирование наилучшей модели. Для теста была выбрана модель:
  CatBoostClassifier, ROC-AUC = 0.920756, iterations=1000, learning_rate=0.5, max_depth=2.

• Показатели на тестовой выборке:

1. Accuracy: 0.9114139693356048
2. AUC-ROC: 0.9083790293650387

• Самыми важными признаками являются:

1. Время пользования услугами оператора (61.7%),
2. Сумма потрачененных средств на услуги (16.7%),
3. Ежемесячные траты на услуги (9.6%)

• Рекомендации для оператор связи «Ниединогоразрыва.ком»:

1. Стоит обращать особое внимание на новых пользователей. Возможно стоит предлагать новичкам более выгодные тарифы и услуги.
2. Также стоит уделить внимание группам пользователей с высокими показателями оплат услуг в месяц. Хорошим вариантом будет предоставлять скидку на оплату за несколько месяцев и год. Хорошим спосбом удержать клиентов - ввести бонусную программу по типу "чем больше платишь - тем больше скидка/бонусы".

• Отличия от исходного плана работ:

1. приводить названия столбцов к нижнему регистру не понадобилось;
2. выбросы не были обнаружены;
3. были добавлены столцы с целевым признаком и временем пользования услугами;
4. обучалось 6 моделей, вместо заявленных 2-3, так как на начальном этапе не удавалось добиться нужных показателей метрики и пришлось импровизировать с различными моделями;
5. после тестирования была добавлена визуализация важности признаков.

Трудностей в работе не встречалось, все этапы выполнения проекта являются важными и нельзя опускать ни один из них.